arXiv:2606.06322v1 Announce Type: new Abstract: GUI agents - vision-based models that control desktops, web browsers, and mobile devices through graphical user interfaces - promise to automate a wide range of digital tasks. While million-scale datasets have enabled substantial progress on click-grounding, drag grounding (e.g. drag-and-drop, swipe, highlight) data remains an order of magnitude smaller and current models fall short on complex drag-based interactions. We introduce DragOn, a drag grounding benchmark and training dataset covering four domains: text highlighting, cell selection, ele
The development of DragOn directly addresses a current critical gap in GUI agent training data and benchmarks for complex drag-based interactions, which existing datasets are not adequately covering.
This new benchmark and dataset accelerates the development of more capable and autonomous AI agents by providing essential training material for a broader range of human-computer interactions beyond simple clicks.
The availability of DragOn means future GUI agents will be better equipped to handle nuanced drag-and-drop, swipe, and highlighting tasks, moving closer to automating complex workflows.
- · AI agent developers
- · Automation software companies
- · Cloud computing providers
- · Enterprise SaaS companies
- · Manual data entry services
- · Companies relying on simple GUI automation
Increased accuracy and capability of AI agents in automating desktop and web tasks involving drag interactions.
Broader adoption of AI agents in white-collar workflows, leading to efficiency gains and workforce restructuring.
Enhanced competition in the AI agent market as development barriers for complex GUI control are lowered, potentially accelerating the collapse of existing SaaS layers by autonomous systems.
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Read at arXiv cs.AI